, w T ), to a binary output space Y = {−, +}.

our experiments will focus on the case in which the w i 's represent word counts in a given document, in a corpus of vocabulary size T .


MNB-FM attempts to improve the noisy estimates θ + w and θ − w utilizing the robust estimate for P (w) computed over unlabeled data.

mNB-Fm proceeds by assuming the mLEs for P (w) (computed over unlabeled data), P t (+), and P t (−) are correct, and reestimates θ + w and θ − w under the constraint in Equation 2.


SFE also augments multinomial Naive Bayes with the frequency information P (w), although in a manner distinct from MNB-FM.

sFE uses the equality P (+|w) = P (+, w)/P (w) and estimates the rhs using P (w) computed over all the unlabeled data, rather than using only labeled data as in standard MNB.


The clue-based approach was slightly better than the distribution-based approach.

the clue-based approach is good at predicting large and small labels, whereas the distributionbased approach is good at predicting normal labels.


We can find a number of sentences like if you won the 10-million-dollar lottery, ....

people talk about a large amount of money even if they did not win any money at all.


After contextualization, we apply singular value decomposition (SVD) for dimensionality reduction to reduce the number of model parameters, keeping the k most significant singular values and vectors.

we apply SVD to the m-byn term-context matrix M produced by the SIM-PLE method, resulting in the truncated matrices where U k is a m-by-k matrix, Σ k is k-by-k, and V k is n-by-k.


We do not prescribe what a term must be.

we do not place any particular syntactic restrictions on what constitutes an allowable term, beyond the requirement that terms must be contiguous sequences of words in both source and target languages.


In the current work, this particular insight is used to solve the data sparsity problem in the sentiment analysis by leveraging unlabelled monolingual corpora.

experiments are performed to investigate whether features developed from manually crafted clusterings (coming from WordNet) can be replaced by those generated from clustering based on syntagmatic properties.


We hope that our techniques and datasets will spur further research into this area.

more research is needed to handle more complex matches between database and textual relations, and to handle more complex natural language queries.


(2009), we assign categories to bigram and trigram items separately according to their frequency counts.

top-10% most frequent items are assigned to the category of High Frequency (HF); otherwise if an item is among top 20%, we assign it to the category of Middle Frequency (MF); otherwise the category of Low Frequency (LF).


From the table, we can see that incorporating semisupervised features decreases parsing speed, but the semi-supervised parser still has the advantage of efficiency over other parsers.

the semi-supervised parser is 7 times faster than the Berkeley parser.


However, care must be taken: when the improved state-tracker is installed into a dialog system and used to drive action selection, the distribution of the resulting dialog data (which is an input for the state tracker) will change.

it is known a priori that the train and test distributions will be mismatched.


However, unlike in traditional maximum entropy models such as the fixed-position model above, these features functions are dynamically defined when presented with each turn.

for a turn with G hypotheses, we define φ i (x, y = g) = x g i , where y ranges over the set of possible dialog states G + 1 (and as above i ∈ 1 .


We may find that in this example whether the insertion or the removal of connective but would not lead to a redundant or missing information between the above two sentences.

discourse connectives can be inserted between or removed from two sentences without changing the semantic relations between them in some cases.


In this paper, we propose a multi-task learning based method to improve the performance of implicit discourse relation recognition (as main task) with the help of relevant auxiliary tasks.

the main task is to recognize the implicit discourse relations based on genuine implicit discourse data and the auxiliary task is to recognize the implicit discourse relations based on synthetic implicit discourse data.


As a result, the model can be optimized by the similar shared part without bringing noise in the dissimilar part.

in this work, we use alternating structure optimization (ASO) (Ando and Zhang, 2005a) to construct the multi-task learning framework.


the ASO method adopted singular value decomposition (SVD) to obtain Θ and m predictors that minimize the empirical risk summed over all the m problems.


Under the ASO-based multitask learning framework, the model of main task learns from the shared part of main task and auxiliary tasks.

we adopt multiple binary classification to build model for main task.


Specifically, we adopt multiple binary classification to build model for main task.

for each discourse relation, we build a binary classifier.


We hypothesize that synthetical implicit data would contribute to the main task, i.e., the implicit discourse relation classification.

the natural implicit data (i.e., imp) are used to create main task and the synthetical implicit data (exp or BLLIP) are used to create auxiliary tasks for the purpose of optimizing the objective functions of main task.


Our best multi-task learning system achieves an averaged F 1 improvement of 5.86% over the best single task learning system on the top level of PDTB relations.

for Class One-tailed t-test results Comp.


And for different connectives, the ambiguity of relation senses is quite different.

the most frequent sense is not always the only sense that a connective expresses.


for the relations Comp., Cont., Exp., Temp., our best multi-task learning system achieves 4.26%, 7.06%, 8.8%, and 3.34% F 1 improvements over a state of the art baseline system.


Following (Joty et al., 2012), we implement a probabilistic CKY-like bottom-up algorithm for computing the most likely parse using dynamic programming.

with n discourse units, we use the upper-triangular portion of the n×n dynamic programming table D. Given U x (0) and U x (1) are the start and end EDU Ids of unit U x : where, k = argmax Note that, in contrast to previous studies on document-level parsing (Hernault et al., 2010;Subba and Di-Eugenio, 2009;Marcu, 2000b), which use a greedy algorithm, our approach finds a discourse tree that is globally optimal.


In this process, each sentence in a document except the first and the last will be associated with two DTs: one with the previous sentence (say DT p ) and one with the next (say DT n ).

for each non-boundary sentence, we will have two decisions: one from DT p and one from DT n .


Following (Subba and Di-Eugenio, 2009) on the Instructional corpus, we use 26 relations, and treat the reversals of non-commutative relations as separate relations.

goal-Act and Act-goal are considered as two different relations.


We can formulate this more rigorously: since the object space X is not directly observable, we do not know its complexity.

when using a mapping to a too small feature space, the classifier cannot capture the distribution very well: the data is too noisy.


In this paper, we explore catenae instead of paths for ad hoc IR due to their suitability for efficient IR models and flexible representation of language semantics.

we note that catenae identify words that can be omitted in elliptical constructions .


Another major observation (Table 5) is that mixing baseline heuristic filters with a supervised approach is not as successful as supervised selection alone.

performance decreases for filtered governor-dependent pairs.


This has painful consequences such as high frequency of parsing errors related to coordination.

coordination is a pending problem in dependency analysis of natural languages.


, A n ) ∈ A n , for a given annotation task, is a vector of annotations, one for each annotator.

a is an  Table 1: a profile with a collective annotation.


• F is neutral if it treats all items symmetrically, i.e., if for every two items j and j with the same set of possible categories (i.e., with C j = C j ) and for every profile A, it is the case that whenever N A j:a = N A j :a for all answers a ∈ A j = A j , then F (A) j = F (A) j .

if the patterns of individual annotations of j and j are the same, then also their collective annotation should coincide.


We first want to explore the following idea: If a given annotator annotates most items with 0, then we might want to assign less significance to that choice for any particular item.

4 if an annotator appears to be biased towards a particular category, then we might want to try to correct for this bias during aggregation.


Given profile A, the collective category for item j will be 1 in case a i,j =1 w 1 i > a i,j =0 w 0 i , and 0 otherwise.

5 we compute the overall weight for category 1 by adding up the corresponding weights for those coders that chose 1 for item j, and we do accordingly for the overall weight for category 0; finally, we choose as collective category that category with the larger overall weight.


Here are three ways of making the intuitive idea of bias correction concrete: (1) The complement-based BCM rule (ComBCM) is defined by weights .

the weight of annotator i for category X is equal to her relative frequency of having chosen the other category 1−X.


Note that greedy consensus rules, as defined here, are both anonymous and neutral.

it is important not to confuse possible skewness of the data with a violation of neutrality of the aggregator.


But this is too simplistic: any singleton ANN = {i} will result in perfect agreement.

while we can easily maximise agreement, doing so in a naïve way means ignoring most of the information collected.


That is, while we can easily maximise agreement, doing so in a naïve way means ignoring most of the information collected.

we face the following dilemma: • On the one hand, we should choose a small set ANN (i.e., select few annotators to base our collective annotation on), as that will allow us to increase the (average) reliability of the annotators taken into account.


(2013) have discussed the use of these methods in the context of crowdsourcing.

they have designed an experiment in which the ground truth is defined unambiguously and known to the experiment designer, so as to be able to extract realistic models of distortion from the data collected in a crowdsourcing exercise.


The strategy within ParGram is to abstract away from the particular surface morphosyntactic expression and aim at parallelism at the level of f-structure.

monoclausal predications are analyzed via a simple f-structure whether they consist of periphrastically formed complex predicates (Urdu, Figure 3), a simple verb (English, Figure 4), or a morphologically derived form (Turkish, Figure 5).


(2012b) who obtain distinct representations for the textual and visual modalities.

they extract a visual space from images contained in the ESP-Game data set (von Ahn and Dabbish, 2004) and a text-based semantic space from a large corpus collection totaling approximately two billion words.


TER measures the amount of edits required to change a system output into one of the reference translations.

: TER = # of edits average # of reference words Possible edits include insertion, substitution deletion and shifts of words.


More formally, we enable selective propagation of seed translations on weakly comparable corpora, by 1) clarifying the detailed meaning of relational information of co-occurring entities, and 2) identifying the contexts of the relational information using statement-level context comparison.

we propagate the translation score of a known translation pair to a neighbor pair if the semantics of their relations in English and Chinese corpora are equivalent to accurately propagate the scores.


To the best of our knowledge, our approach is the first to translate a broad range of multilingual relations and exploit them to enhance NE translation.

our approach leverages semantically similar document pairs to exclude incomparable parts that appear in one language only.


Other interesting approaches such as (Klementiev and Roth, 2006;Kim et al., 2012) rely on temporal distributions of entities.

two entities are considered to be similar if the two entities in different languages have similar occurrence distributions over time.


∆T N (e E , e C )= ∑ where B * is a subset of B ⊂ S i E ×S j C such that the connected statements mention e E and e C , and B is the greedy approximate solution of maximum bipartite matching for the set S i E of statements of d i E and the set S j C of statements of d j C .

b * is a set of matching statement pairs mentioning the translation target e E and e C in the document pair.


For each attribute, we aim to learn an extractor to find the missing value from the unstructured article texts in the target Wikipedia by using the rich information in the source language.

we treat this cross-lingual information extraction task as a transfer learning-based binary classification problem.


TrAdaBoost iteratively updates the weights of all training instances to optimize the prediction error.

the weight-updating strategy for the source instances is decided by the loss on the target instances.


After that, we extract the adjacent positive tokens in text as the predict value.

the longest positive token sequence and the one that contains other positive token sequences are preferred in extraction.


Our experiments are performed on four attributes, which are occupation, nationality, alma mater in TEMPLATE PERSON, and country in TEMPLATE FILM.

we extract values from the first two paragraphs of the texts because they usually contain most of the valuable information.


The generative process of ASMs is, however, different from our model.

type-1 ASMs use asymmetric hyper-parameters for aspects while type-2 assumes that sentiments and aspects are emitted in the same sentence.


Next, we throw light on the issue of choosing the number of top k phrases from the ranked candidate n-grams.

we want to analyze the coverage of our proposed ranking based on relevance models.


The strength of this restriction depends on the size of A topic .

a flaw such as in-universe is restricted to a very narrow selection of articles, while a flaw such as copy edit can be applied to most articles and rather shows a topical preference due to reasons outlined in Section 3.


We can assign a higher weight to features related to characters closer to the current target character.

for this example, given the current target character " ¥", we can assign higher weight to features generated from features from the proximal context "¥ ¶", and lower weight to features extracted from distal contexts.


As discussed previously, a good source for mining such templates are CQA sites.

in this study we utilize all questions submitted to Yahoo!


To assess how relevant a given comparable relation is to an article, we model the relation's typical context as a distribution over latent semantic topics.

we utilize Latent Dirichlet Allocation (LDA) (Blei et al., 2003) to infer latent topics in texts.


We automatically induce this feature from the Wikipedia abstract in each DBPedia entry.

we construct a histogram of male and female pronouns: he and his vs. she and her.


In our case, we over sample to help the classifier explore the large space of negative examples.

we sample 2,000 negative examples and duplicate the positive set to reach a similar number.


One primary benefit of this property is on enriching vocabulary coverage.

the new features of various trigrams only occurring in unlabeled data can be discovered.


The proposed semi-supervised approach is able to discover their label information with the help of a similarity graph.

it learns the label distributions from similar words (neighborhoods), e.g., "上海港" (Shanghai port), "保 护 区" (protection zone), "九 点 七 亿" (nine hundred and seventy million).


This paper develops a phrase-based translation model which aims to address the above shortcomings of the phrase-based translation pipeline.

we formulate translation using inverse transduction grammar (ITG), and seek to learn an ITG from parallel corpora.


parameters affects the rate at which the number of types grows compared to the number of tokens.

as the discount a or the concentration b parameters increases we expect for a relative increase in the number of types.


Different from Brown's generative model (Brown et al., 1993), the loglinear model does not assume strong independency holds, and allows arbitrary features to be integrated into the model easily.

it can transform complex language translation into feature engineering: it can achieve high translation performance if reasonable features are chosen and appropriate parameters are assigned for the weight vector.


We define the embedding of α as the concatenation of the word embedding of each word in α.

for the non-terminal in α, we define its word embedding as the vector whose components are 0.1; and we define the word embedding of "N U LL" as 0.


AdNN-Hiero-E gains about 0.7 BLEU scores on  In addition, to investigate the effect of different feature settings on AdNN, we alternatively design another setting for h in Eq.

(5): we use the default features for both h and h. the language model of a rule for h is locally calculated without the contexts out of the rule as described in (Chiang, 2007).


In face of the problem, we propose an efficient phrase table combination method.

we train a Bayesian phrasal inversion transduction grammars for each domain separately.


First, phrase pairs are extracted from each domain without interfering with other domains.

we employ the nonparametric Bayesian phrasal inversion transduction grammar (ITG) of Neubig et al.


A position w ∈ pos(t) is a leaf (in t) if w1 / ∈ pos(t).


We decide to attach an EC to its lowest solid ancestor node.

the EC is moved up to the first solid node in the syntactic tree.


This paper proposes a nonparametric Bayesian method for inducing Part-of-Speech (POS) tags in dependency trees to improve the performance of statistical machine translation (SMT).

we extend the monolingual infinite tree model (Finkel et al., 2007) to a bilingual scenario: each hidden state (POS tag) of a source-side dependency tree emits a source word together with its aligned target word, either jointly (joint model), or independently (independent model).


In the following, we overview the infinite tree model, which is the basis of our proposed model.

we will describe the independent children   (Finkel et al., 2007), where children are dependent only on their parents, used in our proposed model 1 .


We propose a bilingual variant of the infinite tree model, the bilingual infinite tree model, which utilizes information from the other language.

the proposed model introduces bilingual observations by embedding the aligned target words in the source-side dependency trees.


ϕ ′ k is distributed according to a prior distribution H ′ .

the independent model is formally defined as follows: When multiple target words are aligned to a single source word, each aligned word is generated separately from observation distribution parameterized by ϕ ′ k .


In the independent model, we introduce observation variables (e.g., x ′ t and x ′′ t ) and parameters (e.g., ϕ ′ k and ϕ ′′ k ) for each information.

x ′ t and ϕ ′ k are introduced for the surface form of aligned words, and x ′′ t and ϕ ′′ k for the POS of aligned words.


(2008) showed that beam sampling is more robust to initialization and hyperparameter choice than Gibbs sampling.

we introduce an auxiliary variable u t for each node in a dependency tree to limit the number of possible transitions.


in polynomial time which is very short in practice).

sampling, which NP-hard for alternative models such as Markov Random Fields (MRFs), is efficient, theoretically and practically, for DPPs.


We align publicly available daily stock price data from Yahoo Finance with the Reuters news using a method to avoid back-casting.

we use the daily adjusted closing price -the price quoted at the end of a trading day (4PM US Eastern Time), then adjusted by dividends, stock split, and other corporate actions.


By learning word embeddings and composition matrices on more data, the model is likely to generalise better.

for the more complex models, where the composition functions are conditioned on various CCG parameters, this should Training Model Regular Pretraining CCAE-A 77.8 79.5 CCAE-B 76.9 79.8 CCAE-C 77.1 81.0 CCAE-D 76.9 79.7 Table 4: Effect of pretraining on model performance on the SP dataset.


In this section, we will introduce a new method for amortizing inference costs over time.

the key observation that leads to this theorem stems from the structured margin δ for an inference problem p ∼ [P ], which is defined as follows: for all feasible y, we have c t p y p ≥ c t p y + δ.


For a new inference problem q ∼ [P ], we define ∆ as the maximum change in objective value that can be effected by an assignment that is not the solution.

∆ = max Before stating the theorem, we will provide an intuitive explanation for it.


For any feasible y, the objective score assigned to y p is greater than the score assigned to y according to problem q.


Even with the increased ∆, if the condition of the theorem holds, then the rest of the proof follows and hence the new problem will have the same solution.

we can solve relaxed, tractable variants of the maximization in Equation 4 and still retain the guarantees provided by the theorem.


The goal is to solve an integer linear program q, which is defined as We partition the constraints into two sets, say The assumption is that in the absence the constraints C 2 , the inference problem becomes computationally easier to solve.

we can assume the existence of a subroutine that can efficiently compute the solution of the relaxed problem q : We define Lagrange multipliers Λ ≥ 0, with one λ i for each constraint in C 2 .


In short, through a series of experiments we show how MTR clustering provides additional information to SSL on the target domain utterances, and greatly impacts semantic tagging performance.

we analyze MTR's performance on two different types of semantic tags: named-entities and descriptive tags as shown in Table 1.


First, GUSP leverages the target database to constrain the search space.

it defines the semantic states based on the database schema, and derives lexical-trigger scores from database elements to bootstrap learning.


While syntax may not always align perfectly with semantics, it is still highly informative about the latter.

dependency edges are often indicative of semantic relations.


We thus chose to apply feature-rich EM (Berg-Kirkpatrick et al., 2010) in GUSP, which enabled the use of more generalizable features.

gUSP defines a probability distribution over dependency tree d and semantic parse z by where f i and w i are features and their weights, and Z is the normalization constant that sums over all possible d, z (over the same unlabeled tree).


Since the GUSP model factors over nodes and edges, learning and inference can be done efficiently using EM and dynamic programming.

the MAP parse and expectations can be computed by tree-Viterbi and inside-outside (Petrov and Klein, 2008).


For the GUSP system, we set the hyperparameters from initial experiments on the development set, and used them in all subsequent experiments.

we set α = 50 and β = −0.1, and ran three iterations of feature-rich EM with an L 2 prior of 10 over the feature weights.


Even though we do not have sentiment relevance annotations, there are sources of metadata about the movie domain that we can leverage for distant supervision.

movie databases like IMDb contain both metadata about the plot, in particular the characters of a movie, and metadata about the "creators" who were involved in the production of the movie: actors, writers, directors, and composers.


We use the emotion-tagged dialogue corpus to learn eight translation models and language models, each of which is specialized in generating the response that elicits one of the eight emotions (Plutchik, 1980).

the models are learned from utterances preceding ones that are tagged with emotional category.


The two models are then merged as the weighted linear interpolation.

we use tmcombine.py script provided by Moses for the interpolation of trans-lation models (Sennrich, 2012).


In this evaluation, the system is provided with the utterance and the goal emotion in the test data and the generated responses are evaluated through BLEU score.

we conducted two-fold cross-validation to optimize the weights of our method.


While the combination of modalities still helps, the improvement is smaller than the one obtained during the utterance-level classification.

the combined effect of acoustic and visual features improves significantly over the individual modalities.


Unsupervised methods have been widely used.

recently several optimization approaches have demonstrated competitive performance for extractive summarization task.


Even though the submodularity framework is quite general, it has limitations in its expressivity.

it cannot capture redundancy constraints that depend on pairwise dissimilarities between sentences.


We therefore decided to formalize the task of simultaneously performing sentence extraction and compression as a submodular maximization problem.

we extract subsentences for making the summary directly from all available subsentences in the documents and not in a stepwise fashion.


4, We can ignore the second term of the reward function while looking for the MDS in a sentence because the number of sentences is the same for every MDS in a sentence.

the gain function of adding a subtree to a summary can be represented as the sum of gains for words: where f req t (w) is the number of ws in subtree t, and gain S (w) is the gain of adding the word w to the summary S. Our algorithm is based on dynamic programming, and it selects a subtree that maximizes the gain function per cost.


The score reduction can change the order of the gains of the union.

it is possible that another union without word overlaps will have a larger gain.


∀T ⊆ B, ∃b ∈ B, b = union(T ), because we have an equivalent tree b ∈ B for each union of trees T in a valid set B.

for any set of subtrees, we have an equivalent set of subtrees, where b i ∈ B i .


On the average of all results with different parameters, our method also gains more than 2.0% improvements on all baselines.

our model achieves the most significant gains on Chinese, where the improvements are 4.5%/12.0% on direct/indirect projection base-  shows the results of the baselines: a unsupervised method baseline (Klein and Manning, 2004)(D-MV); a single-source projection method baseline (Hwa et al., 2005) (DPA) and its improvement (Jiang and Liu, 2010)(WPC); two multisource baselines (McDonald et al., 2011)(Transfer) and (Naseem et al., 2012)(Selective).


It is especially noteworthy that the more training data is utilized the more superiority our model enjoys.

because our method not only utilizes the projection information but also avails itself of the monolingual corpus.


Each of the φ(i, j) edge factors (e.g., the edge between node f 3 and e 4 in Figure 1) overlaps with each of the two CRF models over one vertex (e.g., f 3 on Chinese side and e 4 on English side), and we seek agreement with the Chinese CRF model over tag assignment of f j , and similarly for e i on English side.

no direct agreement between the two CRF models is enforced, but they both need to agree with the bilingual edge factors.


One special note is that after each iteration when we consider updates to the dual constraint for entity tags, we only check tag agreements for cross-lingual edge factors that have an alignment assignment value of 1.

cross-lingual edges that are not aligned do not affect bilingual NER tagging.


The BI-NER model with soft agreement constraints, however, significantly outperforms all baselines.

it achieves an absolute F 1 improvement of 6.7% in Chinese and 2.1% in English over the CRF monolingual baselines.


Similar definition has been shown effective in characterizing social distance in social networks extracted from communication data (Lin et al., 2012;Wen and Lin, 2010).

it is dist(i, j) = , where v 1 , ..., v k are the nodes on the shortest path from user i to user j, and strength(v k , v k+1 ) measures the strength of interactions between v k and v k+1 as: , where X ij is the total interactions between user i and j, including both retweeting and mentioning (If X ij < 10, we set strength(i, j) = 0).


Overall, the results demonstrate the effectiveness of our proposed methods.

comparing "HomB" and "HetB", "HomE" and "HetE", we can see that the semantic features based on heterogeneous networks have advantages over those based on homogeneous networks.


We formalize lexical scale impurity as the average absolute difference of scale values between two predicate paths under the same frame.

we want a token-level posterior expectation which is taken over pairs of path instances (i, j) where both paths w i , w j are in M , the set of verb paths that were matched between the lexicons.


There are numerous areas for future work, such as: using verb dictionaries as semi-supervised seeds or priors; interactive learning between political science researchers and unsupervised algorithms; building low-dimensional scaling, or hierarchical structure, into the model; and learning the actor lists to handle changing real-world situations and new domains.

adding more supervision to the model will be crucial to improve semantic quality and make it useful for researchers.


One reason is that allowing paths longer than 2 between oov and labeled nodes causes more noise to propagate into the graph.

a paraphrase of a paraphrase of a paraphrase is not necessarily a useful paraphrase for an oov as the translation may no longer be a valid one.


On the basis of PASTR and IC-PASTR, we further integrate our MEPD model into translation.

we take the score of the MEPD model as another informative feature for the decoder to distinguish good target-side-like PASs from bad ones.


The "*" and "#" denote that the result is significantly better than BTG and PASTR respectively (p<0.01).

after integrating the inside context information of PAS into transformation, we can see that system IC-PASTR significantly outperforms system PASTR by 0.71 BLEU points.


We hypothesize that: Hypothesis: Mother tongue interference is so strong that the relations in a language family are preserved in texts written in another language.

mother tongue interference is so strong that one can reconstruct a language fam-ily tree from non-native texts.


The where w t,i and |D i | denote the tth token in D i and the number of tokens in D i , respectively, since we use the trigram language model.

then, the distance from the distance is determined based on the ratio of the probabilities that each language model generates the language data.


This finding in turn reveals that the consecutive repetitions of nouns occur less in the Italic Englishes.

the length tends to be shorter than in the others where we define the length as the number of consecutive repetitions of common nouns (for example, the length of orange juice is one because a noun is consecutively repeated once).


In addition, the length of noun-noun compounds and the position of adverbs should also be considered in native language identification.

the former can be modeled by the Poisson distribution as follows.


When performing inference, we wish to select the output sequence with the highest probability, given the input sequence x and the weight vector Θ (i.e., MAP inference).

we want an assignment α = arg max α p(α | x, Θ).


As stated in Section 3.2, a legal assignment α corresponds to a path from start to end; moreover, the sum of the weights on that path is equal to log p(α | x, Θ) + log Z(x).

a longer path corresponds to an assignment with greater probability.


In this section, we discuss our instantiation of the model presented in the previous section.

we describe our replacement generators and features.


In this work, we study the coherence of the predicate and argument realization in discourse.

we have followed a similar approach to the one proposed by Dahl et al.


Following this approach, we have developed a deterministic algorithm that obtains competitive results with respect to supervised methods.

our system can be applied to any predicate without training data.


4 ImpAr algorithm Exploring the training dataset of BNB, we observed a very strong discourse effect on the implicit and explicit argument fillers of the predicates.

if several instances of the same predicate appear in a well-written discourse, it is very likely that they maintain the same argument fillers.


The remaining 40% of all implicit arguments correspond to first occurrences of their predicates.

these fillers can not be recovered from previous instances of their predicates.


They refer to previous predicate instances assuming that the reader already recalls the involved participants.

the filler of the different instances of a predicate argument maintain a certain discourse coherence.


For the following instances, without an explicit filler for a particular argument position, the algorithm repeats the same selection process and compares the new implicit candidate with the default one.

the default implicit argument of a predicate with no antecedent can change every time the algorithm finds a filler with a greater salience.


We tried to mimic a damping factor following this distribution.

to maintain high score salience for the near sentences while strongly decreasing them in the subsequent ones.


Additionally, the highest loss appears when the default fillers are ruled out (Exp3).

it also seems that the explicit information from previous predicates provides the most correct evidence (Exp2).


Based on these observations, we have developed a new deterministic algorithm, ImpAr, that obtains very competitive and robust performances with respect to supervised approaches.

it can be applied where there is no available manual annotations to train.


Here every source language word would have itself as its representation and every target word would map into a source word that corresponds to it.

we supply the model with a gloss of the target sentence.


For each such triple (a, s, ) ∈ U π , we apply the following disambiguation heuristics: • One sense per page: if another occurrence of a in the same Wikipedia page (independent of the lexical predicate) is linked to a page l, then remove (a, s, ) from U π and add (a, s, l) to D π .

we propagate an existing annotation of a in the same Wikipedia page and apply it to our ambiguous item.


Figure 1: Sample pairs of similar caseframes by relation type, and the similarity score assigned to them by our distributional model.

they are (gov, role) pairs, where gov is a proposition-bearing element, and role is an approximation of a semantic role with gov as its head (See Figure 1 for examples).


One possibility is that human summary writers are able to pack a greater number of salient caseframes into their summaries.

humans are fundamentally relying on centrality just as automatic summarizers do, and are simply able to achieve higher compression ratios by being more succinct.


We chose two relatively high levels of similarity (0.8 and 0.9), and used complete-link agglomerative (i.e., bottom-up) clustering to merge similar caseframes.

each caseframe begins as a separate cluster, and the two most similar clusters are merged at each step until the desired similarity threshold is reached.


Our final goal in this research is to build a headline generation system that, given a news collection, is able to describe it with the most compact, objective and informative headline.

we want the system to be able to: • Generate headlines in an open-domain, unsupervised way, so that it does not need to rely on training data which is expensive to produce.


Finally, in order to understand whether the noisy-OR Bayesian network is useful for generalizing across patterns into latent events, we added a baseline that extracts all patterns from the test collection following the same COLLECTIONTOPAT-TERNS algorithm (including the application of the linguistically motivated heuristics), and then produces a headline straightaway from the most frequent pattern extracted.

the only difference with respect to HEADY is that in this case no generalization through the Noisy-OR network is carried out, and that headlines are generated from patterns directly observed in the test news collections.


The last approach is also concerned with generating restaurant recommendations within an SDS.

their system optimises its performance online, during the interaction, by asking users to provide it with new textual descriptions of concepts, for which it is unsure of the best realisation.


1 Unfortunately, previous approaches fall short in capturing such cross-unit contextual information that could be critical in reordering.

the popular distortion or lexicalized reordering models in phrasebased SMT focus only on making good local prediction (i.e.


Next, we will show that the MOS-related information can help to resolve this ambiguity, by focusing more closely on the first and the second derivations, which are detailed in Tables 1 and 2.

we want to show that the MOS generated by the incorrect derivation does not match the MOS learnt from Fig.


In this paper, we build upon the approach in (Visweswariah et al., 2011) which uses manual word alignments for learning a reordering model.

we show that we can significantly improve reordering performance by using a large number of sentence pairs for which manual word alignments are not available.


Motivated by this fact, we introduce models that allow us to connect the source/target reordering and the word alignments and show that these models help in mutually improving the performance of word alignments and reordering.

we build two models: the first scores reorderings given the source sentence and noisy alignments, the second scores alignments given the noisy source and target reorderings and the source and target sentences themselves.


The main aim of our work is to improve the reordering model by using parallel sentences for which manual word alignments are not available.

we want to generate relatively clean reference reorderings from parallel sentences and use them for training a reordering model.


Note that this source of supervision is noisy -the generated parser may still be incorrect even when it successfully reads all of the input examples.

the parser may interpret the input examples differently from the text specification.


Intuitively, this distribution should assign high probability to good specification trees that can produce C++ code that reads all input examples without errors, we therefore define P (t) as follows: 5 1 the input parser of tree t reads all input examples without error otherwise where Z is a normalization factor and is empirically set to 10 −6 .

p (•) treats all specification trees that pass the input example test as equally probable candidates and inhibits the model from generating trees which fail the test.


In our case, we sample one hidden specification tree t i while holding all other trees t −i fixed: where However directly solving the subproblem (1) in our case is still hard, we therefore use a Metropolis-Hastings sampler that is similarly applied in traditional sentence parsing problems.

the Hastings sampler approximates (1) by first drawing a new t i from a tractable proposal distribution Q instead of P (t i |w, t −i ).


We choose Q to be: Then the probability of accepting the new sample is determined using the typical Metropolis Hastings process.

t i will be accepted to replace the last t i with probability: in which the normalization factors 1/Z are cancelled out.


We use a small set of 8 seed words to bias the search space.

we require each leaf key phrase to contain at least one seed word that identifies the C++ primitive data type (such as "integer", "float", "byte" and "string").


8 The framework repeatedly predicts hidden structures (specification trees in our case) using a structure learner, and trains the structure learner based on the execution feedback of its predictions.

at each iteration the structure learner predicts the most plausible specification tree for each text document: Depending on whether the corresponding code reads all input examples successfully or not, the (w i , t i ) pairs are added as an positive or negative sample to populate a training set.


To address these challenges, we propose a collective inference method that simultaneously resolves a set of mentions.

our model integrates three kinds of similarities, i.e., mention-entry similarity, entry-entry similarity, and mention-mention similarity, to enrich the context for entity linking, and to address irregular mentions that are not covered by the entity-variation dictionary.


This paper presents such an evaluation for a language acquisition domain where explicit HBMs have been proposed: the acquisition of English dative constructions.

we present a detailed, empiricallygrounded model-selection comparison of HBMs vs. a simpler alternative based on clustering along with maximum likelihood estimation that we call linear competition learning (LCL).


In the case of many language acquisition tasks this behavior often takes the form of overgeneralization, but with eventual convergence to some target language given exposure to more data.

in this paper we consider how children acquire English dative verb constructions, comparing HBMs to a simpler alternative, a linear competition learning (LCL) algorithm that models the behavior of a given verb as the linear competition between the evidence for that verb, and the average behavior of verbs belonging to its same class.


The major trend in context-sensitive inference models utilizes latent or class-based methods for context modeling Ritter et al., 2010;Dinu and Lapata, 2010b).

the more recent methods (Ritter et al., 2010;Dinu and Lapata, 2010b) modeled predicates in context as a probability distribution over topics learned by a Latent Dirichlet Allo-cation (LDA) model.


Concretely, denoting by l and r the predicates appearing in the two rule sides, DIRT's reliability score is defined as follows: (1) where ) is a vector similarity measure.

dIRT employs the Lin similarity measure from (Lin, 1998), defined as follows: We note that the general dIRT scheme may be used while employing other "base" vector similarity measures.


Given a pair of word vectors v and v , and any desired "base" vector similarity measure sim (e.g.

sim Lin ), we compute a topic-biased similarity score for each LDA topic t, denoted by ) is computed by applying the original similarity measure over topic-biased versions of v and v , denoted by v t and v t : each value in the biased vector, v t (w), is obtained by weighing the original value v(w) by the relevance of the topic t to the argument word w within d v .


The need for this rather small number of topics is due to the nature of utilization of topics in WT.

topics are leveraged for high-level domain disambiguation, while fine grained wordlevel distributional similarity is computed for each rule under each such domain.


provided a more detailed annotation for each invalid rule application.

they annotated whether the context under which the rule is applied is valid.


This paper addressed the problem of computing context-sensitive reliability scores for predicate inference rules.

we proposed a novel scheme that applies over any base distributional similarity measure which operates at the word level, and computes a single context-insensitive score for a rule.


In future work, we plan to explore the impact of the sense inventory-based network used in our semantic signatures.

we plan to investigate higher coverage inventories such as BabelNet (Navigli and Ponzetto, 2012a), which will handle texts with named entities and rare senses that are not in WordNet, and will also enable cross-lingual semantic similarity.


By making the data from multiple sources easily available with minimal restrictions, we hope that it will be easier to do research that exploits lexical semantics.

we make the data easily accessible to the original wordnet projects, some of whom have already started to merge it into their own resources.


The allover precision for (b) exceeds the precision on the gold standard.


Then we conduct simple query expansion based on the title of the topic and cross-document coreference resolution.

we first add the words from the topic title to the query.


Unlike previous work (Knight and Marcu, 2000;Galley and McKeown, 2007), we do not produce a new parse tree,  but focus on learning to identify the proper set of constituents to be removed.

when a node is dropped from the tree, all words it subsumes will be deleted from the sentence.


postorder) as a sequence of nodes in T , the set L of possible node labels, a scoring function S for evaluating each sentence compression hypothesis, and a beam size N .


We will further assume that the choice of student pair, source text, and judge are marginally independent of one another.

: 5 As a reminder, 0 indicates no preference.


We'll refer to an estimate of P (π|s 1 , s 2 ) as a preference model.

a preference model is a distribution Q(π|s 1 , s 2 ).


We can then distribute the remaining probability mass uniformly among the other two preferences: Another simple model is the direct estimation of each relative ability P (π|s 1 , s 2 ) independently.

for each pair of students s 1 and s 2 , we estimate a separate preference distribution.


Noise ν is drawn from a discretized zero-mean Gaussian with standard deviation σ obs .

p r(ν) is proportional to the value of the probability density function of the zero-mean Gaussian N (0, σ obs ).


Itemresponse theory is the basis of modern testing theory and drives adaptive standardized tests like the Graduate Record Exam (GRE).

the Gaussian parameterization of our IRT models strongly resembles 11 the Thurstone (Thurstone, 1927) and Bradley-Terry- Luce (Bradley and Terry, 1952;Luce, 1959) models of paired comparison and the 1PL normal-ogive and Rasch (Rasch, 1960) models of student testing.


Because our motivation is translation, one significant point of departure between our work and prior related work ( §3) is that we focus on word tokens.

we are not interested only in the question of "has this known word (type) gained a new sense?


The gold-standard edits are with → to and ɛ → the.

the grammatically correct sentence should be The data is similar to the test set.


Besides, a Chinese sentence does not always end with a full stop.

a comma is used frequently as the stop sign of a sentence in Chinese.


We detect sentence boundaries, relying on both full stop and comma signs, since in a Chinese document, comma can be also used to sign the end of a sentence.

we detect sentence boundaries on full stop, exclamatory mark and question mark firstly.


In this paper we propose a global argument inference model to extract those inter-sentence arguments due to the nature of Chinese that it is a discourse-driven pro-drop language with the wide spread of ellipsis and the open flexible sentence structure.

we incorporate various kinds of event relations and the argument semantics into the model in the sentence, discourse and document layers which represent the cohesion of an event or a topic.


In this manner we use the patterns to suggest types for the entities they occur with.

we infer entity types from pattern type signatures.


Interestingly, performance on these artificial data is considerably better than on the real data.

the Bigram model is able to get consistently high F-scores for both the LEARN-ρ and the GOLD-ρ setting.


However, we did not manually intervene on the pairs, since we are interested in training and testing our methods in realistic, noisy conditions.

the need to pre-process corpora to determine which forms are "opaque", and should thus be bypassed by our systems, would greatly reduce their usefulness.


The quality of the corpus-based vectors representing derived test items was determined by collecting human semantic similarity judgments in a crowdsourcing survey.

we use the similarity of a vector to its nearest neighbors (NNs) as a proxy measure of quality.


Table 4: Mean similarity of composed vectors to high-quality corpus-extracted derived-form vectors with negative affixes fulladd and lexfunc significantly outperform stem also in the HR subset (p<.001).

the models provide better approximations of derived forms even when the stem itself should already be a good surrogate.


The Dirichlet serves as the conjugate prior for the Multinomial, meaning that the posterior θ 1 ...θ k |X 1 ...X n is again distributed as a Dirichlet (with updated parameters).

it is instructive to reparameterize the Dirichlet with k + 1 parameters: the parameters α ′ i give the mean of the distribution, and α 0 gives the precision of the distribution.


The single Dirichlet emission parameter per cluster will specify whether this mean is on a peak (large β) or in a valley (small β).

it will control the expected sparsity of the resulting per-language emission multinomials.


Given a language model p(•) and a lexical simplification example, we ranked the list of candidates based on the probability the language model assigns to the sentence with the candidate simplification inserted in context.

we scored each candidate simplification r j by p(s 1 ... s i−1 r j s i+1 ... s n ) and then ranked them based on this score.


In recent data-driven generation research, the focus has somewhat shifted from full data-to-text systems to approaches that isolate well-defined subproblems from the NLG pipeline.

the tasks of surface realization and referring expression generation (REG) have received increasing attention using a number of available annotated data sets (Belz and Kow, 2010;Belz et al., 2011).


For instance, assume 5-gram sequences of words, two words may be normalization equivalences if their n-gram context shares the same two words on the left and the same two words on the right.

they are sharing a wild card pattern such as (word 1 word 2 * word 4 word 5 ).


It automatically selects data for creating a domain-specific LM.

the method constructs LMs using corpora of target and non-target domains and computes a cross-entropy score of an input sentence for estimating the likelihood that the input sentence belongs to the target or non-target domains.


Although there are several approaches for feature selection, e.g., (Sebastiani, 2002), we use a method that is suitable for handling a large number of feature candidates.

we evaluate gappy-phrases based on the information gain that measures the amount of information in bits obtained for class prediction when knowing the presence or absence of a phrase and the corresponding class distribution.


This corresponds to measuring an expected reduction in entropy, i.e., uncertainty associated with a random factor.

the information gain G ∈ R for a gappy-phrase g is defined as where H(C) represents the entropy of the classification, C is a stochastic variable taking a class, X g is a stochastic variable representing the presence (X 1 g ) or absence (X 0 g ) of the phrase g, P (X g ) represents the probability of presence or absence of the phrase g, and H(C|X g ) is the conditional entropy due to the phrase g. We use top-k phrases based on the information gain G. we use the top 40% of phrases to compute the feature values.


We evaluate the sentence-level and documentlevel accuracy of our method using the Japanese dataset.

we evaluate effects of individual features and their combinations, compare with human annotations, and assess performance variations across different sentence lengths and various settings on LM training.


Let ) is consistent with a word alignment A if for all (i, i ) ∈ A, i ∈ I if and only if i ∈ I .

a phrase alignment is consistent with a word alignment if the words in the phrases are aligned only with each other, and not with any outside words.


Firstly, we evaluated our problem report recognizer.

we assessed the effect of excitation polarities and trouble expressions in two settings.


4 However, we assume that the sentiment and topic flow is fairly smooth in general.

for two adjacent EDUs not connected by any of the above three relations, the prior probability of staying at the same topic and sentiment level is higher than picking a new topic and sentiment level (i.e.


As proposed in Section 1, we consider the task of jointly identifying opinion entities and opinion relations.

given a sentence, our goal is to identify spans of opinion expressions, opinion arguments (targets and holders) and their associated linking relations.


We also employ syntactic patterns from dependency trees to generate candidates.

we selected the most common patterns of the shortest dependency paths 2 between an opinion candidate o and an argument candidate a in our dataset, and include all pairs of candidates that satisfy at least one dependency pattern.


In this paper, we present a new approach for learning procedural dialog systems from taskoriented textual resources in combination with light, non-expert supervision.

we assume access to task knowledge in textual form (e.g., instructional web pages) and examples of user intent statements (e.g., search query logs or dialog interactions).


We develop a simple classification approach that is robust to these different types of user behavior.

we learn classifiers that, given the dialog interaction history, predict how to pick the next tree node from the space of all nodes in the dialog trees that define the task knowledge.


We now detail the experiments that investigate the strengths of features in §5.

we first consider the task of classifying whether an author is tolerant or intolerant in discussions.


Most researchers agree that validating results entails that a method should lead to the same overall conclusions rather than producing the exact same numbers (Drummond, 2009;Dalle, 2012;Buchert and Nussbaum, 2012, etc.).

we should strive to reproduce the same answer to a research question by different means, perhaps by re-implementing an algorithm or evaluating it on a new (in domain) data set.


Some observations go beyond our use cases.

the fact that results vary significantly because of details that are not made explicit in our publications.


Furthermore, for generating the random queries, we will use search query logs to learn n-gram models.

we use the following datasets: Q500, QG500: Saha Roy et al.


An edge between two users indicates a contribution from one user to the other.

on a Q&A site, an edge from A to B is established when user B answered a question asked by A, which shows user B is more likely to be an expert than A.


On the other hand, our method explicitly identifies intra-and inter-sentential causal relations between terms/phrases/clauses that have complex structures and uses the identified relations to answer a why-question.

our method considers more complex linguistic structures than those used in the previous studies.


ef 2 is the same as ef 1 except that the types are augmented with the word classes of the corresponding nouns.

ef 2 indicates whether each type of noun-polarity pair exists in the causal relation for each word class.


We assumed that the system had an oracle for judging correctly whether each phrase is a cause part or an effect part.

we judged that a causal relation recognized by BASELINE is correct if both cause and effect parts in the gold standard are adjacent to a c-marker.


Fundamentally, what the classifier predicts is whether the sentence "matches" the question semantically.

does s have the answer that satisfies the semantic constraints provided in the question?


Inspired by the weighted word count baseline, all features except (Ans) are weighted by the IDF value of the question word.

the IDF values help decide the importance of word pairs to the model.


The second type denotes that two words both depend on a third word directly.


In the training process, the constrained hill-climbing algorithm can ensure that the final model is marginalized on the partial alignment links.

in the E step, their method aims to find out the alignments which are consistent to the alignment links provided by syntactic patterns, where there are main two steps involved.


We select precision and recall as the metrics.

to obtain the ground truth, we manually label all opinion targets for each subset.


Therefore, we adopt a self-learning strategy.

we employ a semi-supervised classifier to refine the target results from the first stage, which uses some highly confident target candidates as the initial labeled examples.


• We adopt a self-learning method for refining opinion words/targets generated by Sentiment Graph Walking.

it can remove high-frequency noise terms and capture longtail opinion targets in corpora (Section 4).


The value of feature is defined as follows, x where conf (•) denotes confidence score estimated by RWR, f req(•) has the same meaning as in Section 3.2.

f req(t i , o j , p k ) represents the frequency of pattern p k extracting opinion target t i and opinion word o j .


Empirically, when overlaid with the second sub-graph, we found that it is better to keep the connectivity of this sub-graph as uni-directional.

we only allow edges to go from a predicate to an argument.


we consider an undirected edge between a pair of arguments a 1 and a 2 only if they occurred together in the "a 1 and a 2 " or "a 2 and a 1 " coordination, and assign edge weights as: where − → a 1 and − → a 2 are co-occurrence vectors for a 1 and a 2 respectively.


Objective function: We aim to maximize: where Φ prosody is the scores based on semantic prosody, Φ coord captures the distributional similarity over coordination, and Φ neu controls the sensitivity of connotation detection between positive (negative) and neutral.

soft constraints (edge weights): The weights in the objective function are set as follows: Note that the same w coord (a 1 , a 2 ) has been used in graph propagation described in section 2.2. α controls the sensitivity of connotation detection such that higher value of α will promote neutral connotation over polar ones.


Efficiency One practical problem with ILP is efficiency and scalability.

we found that it becomes nearly impractical to run the ILP formulation including all words in WordNet plus all words in the argument position in Google Web 1T.


We consider a word has positive or negative polarity only if the assignment indicates 1 for the corresponding polarity and 0 for the rest.

we treat all words with fractional assignments over different polarities as neutral.


The above analysis suggests that incorporating deeper linguistic information in the conversion procedure could improve translation quality.

using a morphological analyzer seeems like a promising possibility.


The difference can be found in the objective function O(w, u; D) shown in Eq.

3; DC-ADMM utilizes L 1regularizer as a part of base leaning algorithm Ω(w) = λ 1 ||w|| 1 , while DCwL1-ADMM discards regularizer of base learning algorithm Ω(w), but instead introducing Υ(u) = λ 1 ||u|| 1 .


This paper focuses on daily one-day-ahead prediction of stock index based on the temporal characteristics of topics in Twitter in the recent past.

we propose a non-parametric topic-based sentiment time series approach to analyzing the streaming Twitter data.


This paper showed that Twitter's topic based sentiment can improve the prediction accuracy beyond existing non-topic based approaches.

a non-parametric topicbased sentiment time series approach was proposed for the Twitter stream.


This gives the pairwise ranking criterion: L(d, e) = max{0, 1 − sim(d, e) + sim(d, e )} (2) Alternatively, we can contrast with all its candidate pairs (d, e i ).

we raise the similarity score of true pair sim(d, e) and penalize all the rest sim(d, e i ).


The key difference between the two tasks is that in SP, the target language (the MRL) has very different properties to an NL.

mRs must conform strictly to a particular structure so that they are machine-interpretable.


These examples are particularly interesting, since they point to a "conceptual" use of determiners, as components of the stable and generic meaning of a content word (as opposed to situation-dependent deictic and anaphoric usages): for these determiners the boundary between content and grammatical word is somewhat blurred, and they thus provide a good entry point for testing DSM representations of DPs on a classic similarity task.

we can set up an experiment in which having an effective representation of the determiner is crucial in order to obtain the correct result.


In this work we focus on the task of identifying and disambiguating implicit discourse relations which have no explicit marker.

we present a reformulation of the word pair features that have most often been used for this task in the past, replacing the sparse lexical features with dense aggregated score features.


As mentioned earlier, there are markers that do not fit the simple pattern we use.

some markers always or often appear as the first term of a sentence.


Research in dependency parsing -computational methods to predict such representations -has increased dramatically, due in large part to the availability of dependency treebanks in a number of languages.

the CoNLL shared tasks on dependency parsing have provided over twenty data sets in a standardized format (Buchholz and Marsi, 2006;.


After producing the two converted and four annotated data sets, we performed a harmonization step, where the goal was to maximize consistency of annotation across languages.

we wanted to eliminate cases where the same label was used for different linguistic relations in different languages and, conversely, where one and the same relation was annotated with different labels, both of which could happen accidentally because annotators were allowed to add new labels for the language they were working on.


This is likely the first reliable cross-lingual parsing evaluation.

previous studies could not even report LAS due to differences in treebank annotations.


This may also lead to modifications of the annotation scheme, which should be regarded as preliminary at this point.

with more typologically and morphologically diverse languages being added to the collection, it may be advisable to consistently enforce the principle that content words take function words as dependents, which is currently violated in the analysis of adpositional and copula constructions.


5 While the F 1 improvement is unsurprising, for the first time we can clearly show that the gains are only in a subset of the error types.

tagging improvement will not help for two of the most significant challenges: coordination scope errors, and verb argument selection.


While applying beam-search is relatively straightforward, the main difficulty comes from combining easy-first dependency parsing with perceptron-based global learning.

one needs to guarantee that each parameter update is valid, i.e., the correct action sequence has lower model score than the predicted one 1 .


We show that both easy-first POS tagging and dependency parsing can be improved significantly from beam search and global learning.

on CTB we achieve 94.01% tagging accuracy which is the best result to date 2 for a single tagging model.


Each step, the algorithm chooses the highest score action to perform according to the linear model: Here, is the weight vector and is the feature representation.

( ( ) ( )) denotes features extracted from p i .


Parsing performances on both PTB and CTB are significantly improved with a relatively small beam width (s = 8).

we achieve 86.33% uas on CTB which is 1.54% uas improvement over the greedy baseline parser.


Performance of the matrix completion technique is not so good: predicted scores are quite different than true scores.

performance falls quickly when the number of missing data increases.


In LDA, each topic multinomial distribution φ k is generated by a conjugate Dirichlet prior with parameter β, while each document multinomial distribution θ d is generated by a conjugate Dirichlet prior with parameter α.

θ d,k is the probability of topic k occurring in document D (i.e.


We use this method to evaluate the coherence of query-oriented topics.

the coherence of a topic model T K Θ composed of K topics is: where probabilities of word occurrences and cooccurrences are estimated using an external reference corpus.


In order to define the set of word features, the Web service proposed in (Machado et al., 2009) is used 3 .

it assigns a relevance score to any token present in the set of retrieved Web snippets based on the analysis of left and right token contexts.


The first set of experiments focuses on understanding the behaviour of our methodology within a greedy search strategy for different tunable parameters defined as a tuple < p, K, S(W ik , W jl ) >.

p is the size of the word feature vectors representing both Web snippets and centroids (p = 2..5), K is the number of clusters to be found (K = 2..10) and S(W ik , W jl ) is the collocation measure integrated in the InfoSimba similarity measure.


In order to perform this task, we evaluate performance based on the F b 3 measure defined in (Amigó et al., 2009) over the ODP-239 gold standard dataset proposed in (Carpineto and Romano, 2010).

(Amigó et al., 2009) indicate that common metrics such as the F β -measure are good to assign higher scores to clusters with high homogeneity, but fail to evaluate cluster completeness.


STC: (Zamir and Etzioni, 1998) defined the Suffix Tree Clustering algorithm which is still a difficult standard to beat in the field.

they propose a monothetic clustering technique which merges base clusters with high string overlap.


We described a method to perform coupled information retrieval with a prior knowledge of the downstream QA system.

we coupled IR queries with automatically learned answer features from QA and observed significant improvements in document/passage retrieval and boosted F 1 in answer extraction.


Note that f (x, y) is the bigram frequency, |W i−1 | the sequence length of W i−1 , and is the difference between the empirical Shannon entropy measured on W i and W i−1 , using maximum likelihood estimates.

this empirical esti-mateH(W ) for a sequence W corresponds to: For this equation to work, one needs to estimate other model parameters.


Yet an improved regularized compressor could deliver the result in merely 1.25 second.

even in an 100 × 100 grid search, the regularized compressor algorithm can still finish 4 to 5 times earlier than one single adaptors grammar instance.


The problem is pronounced in languages such as Japanese and Chinese, where words are not separated by whitespaces.

compound nouns pose difficulties to WS since they are productive, and often consist of unknown words.


In this paper, we relook at the problem of pronunciation of English words using native phone set.

we investigate methods of pronouncing English words using Telugu phoneset in the context of Telugu Text-to-Speech.


In such case, as Hillary Clinton is a famous female leader, she may be associated with other Chinese female leaders in Chinese corpus, while such association is rarely observed in English corpus, which causes asymmetry.

hillary Clinton is "atemporal," as Figure 1(b) shows, such that using such dissimilarity against deciding this pair as a correct translation would be harmful.


As there can exist atemporal entities in M ours , we classify them (Section 3.2).

we build two entire graphs and compute R ∞ .


To insert only true positive pairs in M ours , we set threshold higher than the optimized value from the step 4.

we found out that symmetry from M ours closely estimates that from M : observe from Table 1 that, given a manually built ground-truth set M g ⊂ M as described in Section 4.1, S(G e , G c ; M ours ) returns the best symmetry value in two weeks for person entities, which is expectedly the same as the result of S(G e , G c ; M g ).


To connect the different latent space, the basis of the space have to be aligned each other.

topic proportion extracted from the source language must be aligned that from the target language.


Unsupervised Object Matching To connect the different latent space, the basis of the space have to be aligned each other.


Paraphrases, in contrast, may be defined over all possible groundings.

two words or phrases are considered paraphrases if there exists some grounding that they both describe.


It is important to emphasize here that, while we do use Wordnet in our work, our approach does not crucially rely on it, and we use it to obtain only very shallow information.

we use Wordnet to obtain i) POS tags for Google 2-grams, and ii) number of senses for adjectives.


Second, we train classifiers with an ESLsimulated native corpus, because (1) the number of sentences containing a certain error-correction pair is still limited in the ESL corpus and (2) corrected sentences are still difficult to parse correctly due to inherent noise in the Lang-8 corpus.

we use articles collected from Voice of America (VOA) Learning English 8 , which consist of 270k sentences.


All of our proposed methods outperform baselines regarding RAD with high inter-rater agreement.

discSimESL achieves 9.0% and 4.7% higher RAd than THM and RTM, respectively.


In his integrative approach to humor theories, Martin (2007) discusses the connection between tabooness and incongruity resolution.

he discusses the salience hypothesis (Goldstein et al., 1972;Attardo and Raskin, 1991), according to which "the purpose of aggressive and sexual elements in jokes is to make salient the information needed to resolve the incongruity".


The aim of the evaluation is to measure the potential effect of the three types of constraints on funniness of texts.

we test the potential effect of adding the tabooness constraint to the form constraints, and the potential effect of further adding contextual constraints.


The current techniques can be improved, too, in various ways.

we plan to extend the use of n-grams to larger contexts and consider more fine-grained tuning of other constraints, too.


we did not apply any restrictions on the nature of LVCs to be detected.

our task was somewhat more difficult than those found in earlier literature.


However, the differences in encoding of knowledge and multilinguality are the major road blocks in development of such a framework.

in a multilingual country like India, information is available in many different languages.


The predominant unsupervised approach for learning inference rules between templates is via distributional similarity (Lin and Pantel, 2001; Ravichandran and Hovy, 2002;Szpektor and Dagan, 2008).

each argument slot in a template is represented by an argument vector, containing the words (or terms) that instantiate this slot in all of the occurrences of the template in a learning corpus.


all w ∈ L N w , (possibly yielding fractional counts) where N is a global parameter of the model.

the new count that is assigned to each word w is its remaining original count after it has been redistributed (or zero if no original count), plus all the counts that were distributed to it from other words.


For example, 'The game develop eye-hand coordination The game launch eye-hand coordination' is a rule application in this dataset of the rule 'X develop Y → X launch Y', labeled as incorrect, and 'Captain Cook sail to Australia → Captain Cook depart for Australia' is a rule application of the rule 'X sail to Y → X depart for Y', labeled as correct.

we induced two datasets from Zeichner et al.


The words appearing in this document pair would then be an indicative context for the translation equivalence between «bank» eng and «bank» msa .

the translation equivalents present serve as a kind of implicit sense tag.


While wiki-lsi is not yet sufficiently accurate to be used directly in an MT system, it is helpful in producing a list of ranked multilingual translation sets depending on the input context, as part of an intelligent reading aid.

the lookup module would have benefited if syntactic information (e.g.


The distance between them does not matter.

phrase might be at the beginning of the sentence while ′ being at the end of the sentence, but they are considered as co-occurrence phrases.


But instead here, source and target context vectors are translated to pivot context vectors.

if source context vectors have length m and target context vectors have length n, they are converted to pivot context vectors with length z.


We would like to thank the CRITT, CBS group for their help in manual correction of TPR data.

thanks to Barto Mesa and Khristina for helping with Spanish and Danish dataset corrections.


Therefore, we need to enumerate all possible alignments in a hypergraph to obtain the probability sum p(A|H).

generating an alignment from a hypergraph can be modelled as finding a complete hyperedge matching, which is a set of hyperedges without common vertices that matches all vertices.


We can see that most of the subhypergraphs have just less than two vertices and hyperedges.

3 each subhypergraph has 2.0 vertices and 1.4 hy- peredges on average.


Following the above approach, only the reordering rule [0.56 NP PP 1 0] is kept in the above example.

all the NP PP phrases will be reordered, even though the reordering is only slightly preferred in all the training data.


The reason for this behavior is that the rules that were extracted from the semantic role labeled corpus could have isolated semantic roles in them which would not necessarily get connected to the right predicate or argument during decoding.

it is possible for a rule to only contain one or some of the semantic arguments of a predicate, and not even include the predicate itself, and therefore there is no guarantee that the predicate will be translated with the right arguments and in the right order.


Question answering systems have been developed for many languages, but most resources were created for English, which can be a problem when developing a system in another language such as French.

for question classification, no labeled question corpus is available for French, so this paper studies the possibility to use existing English corpora and transfer a classification by translating the question and their labels.


This paper investigates the issue of computing the semantic content of distributional vectors.

we look at the ways we can distributionally express that make is a more general verb than produce, which is itself more general than, for instance, weave.


Glossing over questions of pragmatics, we will assume that a more informative word is more likely to lead to a successful reference than a less informative one.

if Kim owns a cat and a dog, the identifying expression my cat is a better referent than my pet and so cat can be said to have more semantic content than pet.


In order to evaluate the system, we record whether the calculated entropies match the order of each hypernym-hyponym pair.

we count a pair as correctly represented by our system if w 1 is a hypernym of w 2 and KL(w 1 ) < KL(w 2 ) (or, in the case of the baseline, SI(w 1 ) < SI(w 2 ) where SI is selfinformation).


We do it by developing a layer of representation that is applicable to multiple domains.

we add an intermediate layer capturing shallow semantic relations between the input sentence constituents.


Thus, to remedy the bag-of-words failing, we extend the generic DSM model to several relation-specific distributions over syntactic neighborhoods.

one can think of the Structured DSM (SDSM) representation of a word/phrase as several vectors defined over the same vocabulary, each vector representing the word's selectional preferences for its various syntactic arguments.


We used their method to detect misclassified data.

we estimated future error rate by log-loss function.


For different applications, we employ different approaches to determine the value of and .

in our experiment, we assigned the term frequency, a feature frequently used in text classification to , and treated the phase as a constant, since we found the phase makes little contribution to the classification.


Most of the current research has focused on building sentiment analysis applications for the English language (Pang and Lee, 2008;Liu, 2010;Korayem et al., 2012), with much less work on other languages.

there has been little work on sentiment analysis in Arabic (Abbasi et al., 2008;Abdul-Mageed et al., 2011;Abdul-Mageed and Diab, 2012b;Korayem et al., 2012), and very few, considerably small-sized, datasets to work with (Rushdi-Saleh et al., 2011b;Rushdi-Saleh et al., 2011a;Abdul-Mageed and Diab, 2012a;Elarnaoty et al., 2012).


On the basis, we propose a respective way to jointly model these two tasks.

a cotraining algorithm is proposed to improve semi-supervised learning of the two tasks.


In this case, because the writers of the comments are a part of the readers of the news, the writer's emotions on the comments are exactly certain reflection of the reader's emotions on the news.

the comment writer's emotions and the news reader's emotions are strongly related.


In this study, we systematically investigate the relationship between the news reader's emotions and the comment writer's emotions.

we manually analyze their agreement in a corpus collected from a news website.


This motivates our joint modeling in terms of the coarse-grained emotion categories.

we consider the news text and the comment text as two different views of expressing either the news reader's or comment writer's emotions.


For the first part of the task, annotators were asked to label each quote without considering any context.

they were asked to only use the text of the quote itself as evidence for an opinion, not the speaker's prior opinions or the text of the document.


the training set) should decrease the most if the most confidently labeled example is utilized.

the confidence value of each labeled example is measured by the decrease of the error (e.g.


For each noun, we construct a textual representation containing its syntactic dependencies (i.e., its syntactic context).

for each syntactic dependency dep(a, b) (or dep(b, a)) of a target noun a, we create an abstract token 3 deptarget-b (or dep-b-target), whereb becomes the generic string "noun" in case it is another noun; otherwise it is equal to b.


Note that our approach has high levels of accuracy.

even in this task, our system outperforms the pattern matching algorithm proposed by  in terms of Precision and Recall.


Experiments on a number of datasets show that with as little as 25% of the training instances it is possible to obtain similar or superior performance compared to that of the complete datasets.

our active learning query strategies can not only reduce annotation effort but can also result in better quality predictors.


During testing, for each phrase in our test set, we measure semantic relatedness of pairs of words using the IC values estimated from the Arabic Gigaword, and normalize their sum by the number of pairs in the phrase to obtain a measure of Semantic Coherence (SC) of the phrase.

where p is the phrase being evaluated, n is the number of words in it, d is the distance between words, W is the window size (set to 3), and m is the number of all possible w i , w i+d pairs in the phrase given these conditions.


Effect of n-best training size on WER The size of the training n-best lists is crucial to the learning of the ranking model.

it determines the number of training instances per list.


The output of the algorithm is a series of timelines summarization I = {I t } t=T t=1 where I t ⊂ C t Our EHDP model is illustrated in Figure 2.


Different from update summarization, we focus more on the temporal change of trending topics.

we need to automatically detect the "update points" among a myriad of related tweets.


Typically when a subtopic is popular enough, it will create a certain level of surge in the tweet stream.

every surge in the tweet stream can be regarded as an indicator of the appearance of a subtopic that is worthy of being summarized.


We infer the function label of each node in the PS trees based on the morphological features, syntactic environment, and dash-feature (if exist), using deterministic grammar rules (Glinert, 1989).

we compare each edge with a set of templates, and, once finding a template that fits the morphological and syntactic profile of an edge, we assign functions to all daughters.


However, on the out-of-domain BROWN tests, we find that adding Φ deps always adds considerably, and in a statistically significant way, to both LAS and UAS.

not only is Φ phrase+deps better at dependency recovery than its component parts, but Φ phrase+deps+gen is also considerably bet-ter on dependency recovery than Φ phrase+gen , which represents the previous state-of-the-art in this vein of research (Huang, 2008).


3 admits a solution z * s which is spanned by a sparse basis W ⊆ Y s with cardinality at most |W| ≤ O(|A s |).

there is a distribution α with support in W such that z * s = y s ∈W α y s y s .


We draw random binary vectors with P (1) = 1 − δ as in adversarial learning, but deletions are only effective if ξ j = 0 and the weight w j is more than a standard deviation (σ ||w ||) from the mean of the current absolute weight distribution (µ ||w ||).

we only delete the predictive features, with predictivity being relative to the current mean weight.


Although we are only examining the 13.7% of temporal relations that are co-ordinated with a signal, it is important to note the performance of conventional classification approaches on this subset of temporal relations.

the error reduction relative to the baseline that is achieved without signal features is much lower on relations that use signals than on nonsignalled relations (Table 1).


The low scores of D(.5) are found to be due, upon inspection, to the low relevance of keywords.

the comparative relevance of D(.75) vs. D(.5) on the Fisher Corpus is very large (96% vs. 4%).


We consider the task of constructing a geology knowledge base.

our goal is to extract a Rock-TotalOrganicCarbon relation that maps rock formations (e.g., "Barnett Formation") to their total organic carbon (e.g., "6%").


We believe analogical inference over strings has not delivered all his potential yet.

we have observed that there is a huge room for improvements in the aggregation step.


6 Much work has been done on efficient disk-based merge sort.

important is arity, the number of blocks that are merged at once.


We note that in all settings (incremental and non-causal oracle) our adaptation approach matches or significantly outperforms the baseline across multiple evaluation metrics.

the incremental LDA system with 40 topics is the top-scoring system in both clean-text and ASR settings.


Different TK functions are characterized by alternative fragment definitions.

we focus on the Syntactic Tree kernel (STK) (Collins and Duffy, 2002) and a Partial Tree Kernel (PTK) (Moschitti, 2006).


We propose to use dependency relations between words to derive an alternative structural representation.

de- Figure 1: A phrase dependency-based structural representation of a text pair (s1, s2): A woman with a knife is slicing a pepper (s1) vs. A women slicing green pepper (s2) with a high semantic similarity (human judgement score 4.0 out of 5.0).


Our perspective not only relies on the immediacy assumption, but also develops as an outgrowth of other ways that we make meaning outside of the form or function rules of grammar.

halliday and hasan (1976) found that rules of grammar do not explain how cohesive principals engage readers in meaning making across sentences.


Such simplification, however, disregards the fact that semantic equivalence is not only proportional to the number of equivalent terms, but also to their importance.

instead of checking what of a given sentence can be found in the other, current approaches limit the analysis to the amount of lexical elements they share, under the rough assumption that the more the better.


The last two steps serve to eliminate foreign-language content.

the last step serves to filter out the text in Serbian, which at the sentence-level is difficult to automatically discriminate from Croatian.


The selector context Z v of vertex v is a subgraph of G that contains v and all its immediate neighbors.

we consider as context all event men-tions that are in a direct temporal relation with the selected mention.


With this use case in mind, we define the term ambiguity detection (TAD) problem as follows: Given a term and a corresponding topic domain, determine whether the term uniquely references a member of that topic domain.

given a term such as Brave and a category such as film, the task is make a binary decision as to whether all instances of Brave reference a film by that name.


Rather than annotating each term directly, ambiguity was determined by examining actual usage.

for each term, usage examples were extracted from large amounts of Twitter data.


Additionally, ontologies may be apt to list cases of strict ambiguity, rather than practical ambiguity.

an ontology may list a term as ambiguous if there are several potential named entities it could refer to, even if the vast majority of references were to only a single entity.


An important task is to enrich such KBs by extracting more facts from text.

this paper focuses on extracting facts for existing relations.


(2012) point out, debates in public forums differ from congressional debates and company-internal discussions in terms of language use.

online debaters use colorful and emotional language to express their points, which may involve sarcasm, insults, and questioning another debater's assumptions and evidence.


(2011), focusing in particular on ideological debates.

we hypothesize that there are two types of soft extra-linguistic constraints on the stance labels of debate posts that,  if explicitly modeled, could improve a learningbased stance classification system.


We first use the learned stance classifier to classify the test posts as in the first baseline, and then postprocess the labels of the test posts.

we sum up the confidence values 2 assigned to the set of test posts written by the same author for the same debate domain.


More specifically, the stances of the posts written by an author for these three domains are all positively co-related.

if an author supports abortion, it is likely that she supports both gay rights and Obama as well.


The reason can be attributed to the fact that ICs exist more frequently between OBA and ABO and between OBA and GAY than between ABO and GAY.

iCs are seen in all five folds of the data in the first two pairs of domains, whereas they are seen in only two folds in the last pair of domains.


This paper focuses on the second subproblem and leaves the other two as future work.

we purpose to investigate whether characteristic schoolof-thought words exist and whether they can be automatically characterized.


Classifiers, either support vector machine (SVM) or logistic regression (LR), can then be trained with the unified representation (Ngiam et al., 2011;Srivastava and Salakhutdinov, 2012).

the LR classifier is used to make the final prediction in our experiments since it keeps to deliver the best performance.


In this paper, we have provided a new perspective to predict the cQA answer quality: learning an informative unified representation between textual and non-textual features instead of concatenating them simply.

we have proposed a multimodal deep learning framework to form the unified representation.


Our findings support the observations of Cumby and Roth 2003, who point out that kernels introduce a large number of irrelevant features that may be especially harmful in small-data settings, and that, when possible, it is often better to have a set of explicit, relevant features.

it is better to have the right features than all of them.


For outlier detection, means and standard deviations were computed using leave-one-out sampling.

data from individual J were not incorporated into means or standard deviations when considering whether to eliminate J as an outlier.


The predictor H Boost returned by our boosting algorithm is based on a scoring function h : X × Y → R, which, as for standard ensemble algorithms such as AdaBoost, is a convex combination of base scoring functions h t : h = T t=1 α t h t , with α t ≥ 0.

the base scoring functions used in our algorithm have the form these can be derived from the path experts in H by letting h k t (x, y) = 1 h k t (x)=y k .


We denote by D t the distribution over The direction e t selected at the tth round is the one minimizing the directional derivative, that is where t is the average error of h t given by The remaining steps of our algorithm can be determined as in the case of AdaBoost.

given the direction e t , the best step α t is obtained by solving the equation dF (α t−1 +αtet) dαt = 0, which admits the closed-form solution α t = 1 2 log 1− t t .


Since the ensemble methods we presented assume that the predictions of all p experts are available for each example in the training and test sets, we needed to restrict ourselves to the subsets of the data where at least some fixed number of expert predictions were available.


Here, we simulated the case where each expert specialized in predicting some subset of the labels.

we generated 40,000 random sequences over the English alphabet in the same way as for ADS1 and ADS2.


By interleaving these two operations, we arrive at a saddle point of the objective function.

we formulate the following constrained optimization problem, where m ∈ {1, .


One property of the dual variables is that f (v i ; A) is a support vector only if the dual variable η i,y i < 1.

since the dual variables for each instance are guaranteed to sum to one, we have the contribution from non support vectors to the projection matrix A is 0.


This idea is similar to the mini-batch learning, which has been used in large-scale SVM problem (Nelakanti et al., 2013) and deep learning models (Le et al., 2011).

in iteration t, the algorithm randomly chooses a subset of training samples D t to train the model.


From this bipartite graph, we get the maximum weighted matching and use the averaged weight of the matches as the similarity between e i and e j .

we use path_similarity, wup_similarity, res_similarity, jcn_similarity and lin_similarity provided by the nltk.wordnet.similarity (Bird et.


Our approach is to modify the emission probabilities of states to be distributions over topics rather than distributions over words.

instead of generating words via a LM, we generate words from a topic model (TM), where each state maps to a mixture of topics.


3 For each mention m j , let A j denote the set of potential antecedents.

the set of all mentions that precede m j according to the linear order including the root node, or, A j = {m i | i < j}.


We find the weight vector w by online learning using a variant of the structured perceptron (Collins, 2002).

we use the passive-aggressive (PA) algorithm (Crammer et al., 2006), since we found that this performed slightly better in preliminary experiments.


The training data is, however, not annotated with trees, but only with clusters of mentions.


We constrain the antecedent sets such that only trees that correspond to the correct clustering can be built.

letÃ j denote the set of correct antecedents for a mention m j , or Aj = {m0} if mj has no correct antecedent {ai | COREF(ai, mj), ai ∈ Aj} otherwise that is, if mention m j is non-referential or the first mention of its cluster,Ã j contains only the document root.


When only local features are used, greedy search (either with CLE or the best-first decoder) suffices to find the highest scoring tree.

greedy search provides an exact solution to equation 2.


While beam search and early updates have been successfully applied to other NLP applications, our task differs in two important aspects: First, coreference resolution is a much more difficult task, which relies on more (world) knowledge than what is available in the training data.

it is unlikely that we can devise a feature set that is informative enough to allow the weight vector to converge towards a solution that lets the learning algorithm see the entire documents during training, at least in the situation when no external knowledge sources are used.


This is necessary since the update influences what the partial gold structure looks like, and the gold agenda therefore needs to be recreated from the beginning of the document.

after each intermediate LaSO update, the gold agenda is expanded repeatedly from the beginning of the document to the point where the update was made, and is then copied over to seed the prediction agenda.


In order to have a strong local baseline, we applied greedy forward/backward feature selection on the training data using a large set of local feature templates.

the training set of each language was split into two parts where 75% was used for training, and 25% for testing.


In Table 2 we compare the results of the non-local system (This paper) to the best results from the CoNLL 2012 Shared Task.


Our novel method for learning compositional vectors removes these requirements, and as such can more easily be applied to low-resource languages.

we attempt to learn semantics from multilingual data.


With all the advantages of lf, scaling it up to arbitrary sentences, however, leads to several issues.

it is desirable for all practical purposes to limit representation size.


This could improve translation quality, as it brings our training scenario closer to our test scenario (test BLEU is always measured on unsegmented references).

it could address issues with translation length mismatch.


Lattice desegmentation is a non-local lattice transformation.

the morphemes forming a word might span several edges, making desegmentation non-trivial.


We can see a common phenomenon in both of the algorithms: for the first few thresholds, the phrase table becomes smaller and smaller while the translation quality is not much decreased, but the performance jumps a lot at a certain threshold (16 for Significance pruning, 0.8 for BRAE-based one).

the Significance algorithm can safely discard 64% of the phrase table at its threshold 12 with only 0.1 BLEU loss in the overall test.


Given a parallel sentence pair f, e , the first step is to treat f and e as queries, and use IR methods to retrieve relevant documents to enrich contextual information for them.

the ranking model we used is a Vector Space Model (VSM), where the query and document are converted into tf-idf weighted vectors.


Titov (2011) also propose a regularizer to constrain the inter-domain variability.

their regularizer aims to minimize the Kullback-Leibler (KL) distance between the marginal distributions of the learned representations on the source and target domains.


To keep our technique applicable across a large variety of forums with varying availability of non-textual features, we design it to be able to work with minimal availability of non-textual features.

we show that by using post position as the only non-textual feature, we are able to achieve accuracies comparable to supervision-based approaches that use many structural features (Catherine et al., 2013).


We now analyse the performance of our approach against varying parameter settings.

we vary λ and τ values and the dataset size, and experiment with some initialization variations.


Can one use such information to infer more details?

can one exploit indirect clues from an unstructured data source like Twitter to obtain rich, structured user profiles?


As we can see, the property of HOMOPHILY indeed exists among users in the social network with respect to Education and Job attribute, as significant affinity is observed.

the affinity value for Education is 74.3, implying that users connected by a link in the network are 74.3 times more likely affiliated in the same school than as expected if education attributes are randomly assigned.


This person cannot on the spot become ACL president, change the shape of his/her social network, wait until the next morning to speak, or campaign for Rome instead; but he/she can craft the message to be more humorous, more informative, emphasize certain aspects instead of others, and so on.

we investigate whether a different choice of words affects message propagation, controlling for user and topic: would user BarackObama have gotten significantly more (or fewer) retweets if he had used some alternate wording to announce his reelection?


Although we cannot create a parallel universe in which BarackObama tweeted something else 1 , fortunately, a surprising characteristic of Twitter allows us to run a fairly analogous natural experiment: external forces serendipitously provide an environment that resembles the desired controlled setting (DiNardo, 2008).

it turns out to be unexpectedly common for the same user to post different tweets regarding the same URLa good proxy for fine-grained topic 2 -within a relatively short period of time.


As a rough approximation, we check whether the changes in t 2 with respect to t 1 (which share the same topic and author) involve words or parts-of-speech that are associated with high retweet rate in a very large separate sample of unpaired tweets (retweets and replies discarded).

for each word w that appears more than 10 times, we compute the probability that tweets containing w are retweeted more than once, denoted by rspwq.


We find similar behavior across all three graphs.

the posterior estimates converge faster when predicting Democratic than Republican users but it has been trained on an equal number of tweets per class.


In contrast, we demonstrate that highly expressive scoring functions can be used with substantially simpler inference procedures.

we introduce a sampling-based parser that can easily handle arbitrary global features.